计量学报2024，Vol.45Issue(5)：678-684,7.DOI:10.3969/j.issn.1000-1158.2024.05.10
基于多尺度密集连接网络的电容层析成像图像重建
Image Reconstruction of Electrical Capacitance Tomography Based on Multi-scale Densely Connected Network
摘要
Abstract
In order to solve the nonlinear ill-posed inverse problem in electrical capacitance tomography(ECT),a multiscale dense connection network(multi-scale densely connected network,MD-Net)model is proposed.The model consists of a multiscale feature fusion module and a densely connected block to further improve the reconstruction accuracy of images by fusing multiscale features.A flow-type data set is constructed by the MATLAB simulation experiment platform,and the learning and training of the training set are completed by using the nonlinear mapping ability of the densely connected network.The training effect is evaluated by using the test set.Static experiments are conducted on this basis.The simulation and static experiments results show that the method has the highest reconstruction accuracy,good noise immunity,and generalization ability compared with LBP,Landweber iterative algorithm,and other deep learning methods.关键词
两相流测量/电容层析成像/图像重建/深度学习/密集连接网络Key words
two-phase flow measurement/electrical capacitance tomography/image reconstruction/deep learning/densely connected network分类
通用工业技术引用本文复制引用
张立峰,常恩健..基于多尺度密集连接网络的电容层析成像图像重建[J].计量学报,2024,45(5):678-684,7.基金项目
国家自然科学基金(61973115) （61973115）
